Stars
Implementation of papers in 100 lines of code.
Interactive Markov-chain Monte Carlo Javascript demos
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Deep universal probabilistic programming with Python and PyTorch
flwenzel / BayesianSVM.jl
Forked from theogf/BayesianSVM.jlJulia Package of the Bayesian Support Vector Machines Algorithm
'Visualization in Bayesian workflow' by Gabry, Simpson, Vehtari, Betancourt, and Gelman. (JRSS discussion paper and code)
Gaussian Process package based on data augmentation, sparsity and natural gradients
Generalized Dynamic Topic Models using Gaussian Processes
Julia Package of the Bayesian Support Vector Machines Algorithm
R-script for generating canonical diagrams of distributions to be used to describe Bayesian hierarchical models.
Source code of the Bayesian SVM described in the paper by Wenzel et al. "Bayesian Nonlinear Support Vector Machines for Big Data"
Experiment code for Stochastic Gradient Hamiltonian Monte Carlo
Bayesian Modeling and Probabilistic Programming in Python
An Open Source Machine Learning Framework for Everyone
A library of scalable Bayesian generalised linear models with fancy features
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
The Python ensemble sampling toolkit for affine-invariant MCMC
Code to produce demos of Metroplis-Hastings and Hamiltonian Monte Carlo samplers.
Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.
Exploring differentiation with respect to hyperparameters
Fastidious accounting of entropy streams into and out of optimization and sampling algorithms.
Library of common tools for machine learning research.